{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11f98d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import pickle\n",
    "import warnings\n",
    "import xml.etree.ElementTree as ET\n",
    "from plistlib import loads\n",
    "\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import spectral.io.envi as envi\n",
    "import torch\n",
    "from linformer import Linformer\n",
    "from numpy import flip\n",
    "from sklearn import model_selection, svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from AutoGPU import autoGPU\n",
    "from comparetools.global_module.network import CDCNN_network as CDCNN\n",
    "from hongdanfeng.vit_hong import ViT as hongViT\n",
    "from models import (_1DCNN, _2DCNN, _3DCNN, _3DCNN_1DCNN, _3DCNN_AM, PURE1DCNN,\n",
    "                    PURE2DCNN, PURE3DCNN, PURE3DCNN_2AM, SAE, SAE_AM,\n",
    "                    DBDA_network, HamidaEtAl, LeeEtAl, SSRN_network, _2dCNN,\n",
    "                    myknn, mysvm)\n",
    "from NNViT import ViT as NNViT\n",
    "from NViT import ViT as NViT\n",
    "from training_utils import TrainProcess, setup_seed\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b0ec1b5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练比例为 训练集1000 验证集200 测试集-1\n",
      "模型为danfengViT\n"
     ]
    }
   ],
   "source": [
    "dataset = './pathology/data/032370b-20x-roi6'\n",
    "NTr = 1000\n",
    "trialnumber = 1   \n",
    "NVa = 200\n",
    "NTe = -1\n",
    "patchsize = 9\n",
    "modelname = 'danfengViT' \n",
    "gpu_num = 1\n",
    "depth = 5\n",
    "load_bestmodel = 1\n",
    "gpu_ids = -1\n",
    "\n",
    "\n",
    "print('训练比例为 训练集{} 验证集{} 测试集{}'.format(NTr, NVa, NTe))\n",
    "print('模型为{}'.format(modelname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "236f1320",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liyuan/anaconda3/envs/MHSI/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "dataset = './pathology/data/032370b-20x-roi6'\n",
    "dark_ref = envi.open('%s.hdr'%dataset, '%s.raw'%dataset)\n",
    "dark_nparr = np.array(dark_ref.load())\n",
    "# dark_nparr = flip(dark_nparr, 0)\n",
    "tree = ET.parse('%s.xml'%dataset)\n",
    "# plt.figure()\n",
    "# plt.imshow(dark_nparr[:,:,10])\n",
    "# plt.savefig('test.jpg') \n",
    "#     # get root element\n",
    "polygon = tree.getroot().findall('object/polygon')\n",
    "\n",
    "mask = np.zeros((dark_nparr.shape[0], dark_nparr.shape[1]), dtype=\"uint8\")\n",
    "for p in polygon:\n",
    "    x = p.findall('pt/x')\n",
    "    y = p.findall('pt/y')\n",
    "    x_coor = list(map(lambda x:int(x.text), x))\n",
    "    y_coor = list(map(lambda y:int(y.text), y))\n",
    "    c = []\n",
    "    for x, y in zip(x_coor, y_coor):\n",
    "        c.append([x, y])\n",
    "    cor_xy = np.array(c)\n",
    "    # cor_xy = np.hstack(mas(x_coor, y_coor))\n",
    "    cv2.polylines(mask, np.int32([cor_xy]), 1, 1)\n",
    "    cv2.fillPoly(mask, np.int32([cor_xy]), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e71b5d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 1280, 60)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMAGE = np.flipud(dark_nparr)\n",
    "GND = mask\n",
    "IMAGE.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3d1869e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normlize(patch):\n",
    "    norm_patch = np.zeros(shape=patch.shape)\n",
    "    for batchidx in range(patch.shape[0]):\n",
    "        if len(patch.shape) > 2:\n",
    "            norm_patch[batchidx] = normlize(patch[batchidx])\n",
    "        else:\n",
    "         \n",
    "            image = patch[batchidx, :]\n",
    "            image = (image-image.min())/(image.max()-image.min())\n",
    "            norm_patch[batchidx, :] = image\n",
    "    return norm_patch\n",
    "IMAGE = normlize(IMAGE)\n",
    "plt.imshow(IMAGE[:,:,[30,15,10]])\n",
    "IMAGE.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e4a3e1cd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============split finished===============\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/1024 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================== 1310720 All samples to process with 1 multi-process  ====================\n",
      "starting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1024/1024 [06:16<00:00,  2.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "723.9046971797943\n",
      "Patch data has been built!\n"
     ]
    }
   ],
   "source": [
    "NTr, NVa, NTe = 0.0008, 0.0002, 0.999\n",
    "resultpath, imagepath, datapath, rootpath = setpath(dataset, trialnumber , NTr, NVa, NTe, 'NDIS_MODEL')\n",
    "datapath \n",
    "\n",
    "spliteddata = splitdata(GND, datapath , trainnum=NTr, validnum=NVa, testnum=NTe)\n",
    "\n",
    "patchdata = DataPreProcess(IMAGE, patchsize, rootpath, 1).processeddata_patch\n",
    "\n",
    "patchdata = patchdata.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d6625ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('./pathology/032370b/roi6/Split/proportion/Tr_0.0008/Va_0.0002/Te_0.999/1/result/NDIS_MODEL/',\n",
       " './pathology/032370b/roi6/Split/proportion/Tr_0.0008/Va_0.0002/Te_0.999/1/image/NDIS_MODEL/',\n",
       " './pathology/032370b/roi6/Split/proportion/Tr_0.0008/Va_0.0002/Te_0.999/1/',\n",
       " './pathology/032370b/roi6/')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultpath, imagepath, datapath, rootpath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19cc8054",
   "metadata": {},
   "source": [
    "迁移学习"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f35fa032",
   "metadata": {},
   "source": [
    "\n",
    "              'PURE2DCNN':PURE2DCNN,  \n",
    "              'CDCNN':CDCNN,\n",
    "              'SimpledanfengViT': hongViT(mode='ViT'),\n",
    "              \n",
    "              'NNDIS_MODEL':NNViT,\n",
    "              'DanfengViT': hongViT,\n",
    "              'NDIS_MODEL': NViT(depth=3),\n",
    "              'TransGan':NViT,\n",
    "              \n",
    "              \n",
    "             \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "79486ee6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已选择第2张卡，型号为TITAN RTX，24220MB/24220MB显存可用\n",
      "模型实例化\n",
      "读了最佳模型, 继续训练\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:00<00:00, 81.48it/s]\n",
      "100%|██████████| 5/5 [00:00<00:00, 92.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------训练----------------------------\n",
      "[Errno 2] No such file or directory: './pathology/032370b/roi6/Split/proportion/Tr_0.0008/Va_0.0002/Te_0.999/1/result/PURE2DCNN/result.pkl'\n",
      "Training: Epoch[001/025] Loss: 5.28809298 Acc:73.95% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:75.48%\n",
      "best valacc:0.00%\n",
      "Higher Valid Accuracy:%75.48%, Old Valid Accuracy:%0.00%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 5/5 [00:00<00:00, 93.94it/s]\n",
      "100%|██████████| 5/5 [00:00<00:00, 95.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[002/025] Loss: 5.72875918 Acc:74.62% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:75.86%\n",
      "best valacc:75.48%\n",
      "Higher Valid Accuracy:%75.86%, Old Valid Accuracy:%75.48%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[003/025] Loss: 4.12405765 Acc:74.71% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:77.01%\n",
      "best valacc:75.86%\n",
      "Higher Valid Accuracy:%77.01%, Old Valid Accuracy:%75.86%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 5/5 [00:00<00:00, 88.19it/s]\n",
      "100%|██████████| 5/5 [00:00<00:00, 91.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[004/025] Loss: 3.00110233 Acc:76.24% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:77.01%\n",
      "best valacc:77.01%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[005/025] Loss: 2.66640232 Acc:77.00% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:77.78%\n",
      "best valacc:77.01%\n",
      "Higher Valid Accuracy:%77.78%, Old Valid Accuracy:%77.01%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "100%|██████████| 5/5 [00:00<00:00, 82.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[006/025] Loss: 2.28345328 Acc:76.53% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.31%\n",
      "best valacc:77.78%\n",
      "Higher Valid Accuracy:%79.31%, Old Valid Accuracy:%77.78%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[007/025] Loss: 1.89085061 Acc:77.39% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.31%\n",
      "best valacc:79.31%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[008/025] Loss: 1.72795092 Acc:76.34% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:78.93%\n",
      "best valacc:79.31%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[009/025] Loss: 1.35076574 Acc:76.43% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:78.93%\n",
      "best valacc:79.31%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 5/5 [00:00<00:00, 75.04it/s]\n",
      "100%|██████████| 5/5 [00:00<00:00, 86.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[010/025] Loss: 1.26004442 Acc:77.00% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:78.93%\n",
      "best valacc:79.31%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[011/025] Loss: 1.01258499 Acc:78.34% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.31%\n",
      "best valacc:79.31%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[012/025] Loss: 0.98968119 Acc:79.77% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.69%\n",
      "best valacc:79.31%\n",
      "Higher Valid Accuracy:%79.69%, Old Valid Accuracy:%79.31%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[013/025] Loss: 0.86950049 Acc:80.53% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:80.08%\n",
      "best valacc:79.69%\n",
      "Higher Valid Accuracy:%80.08%, Old Valid Accuracy:%79.69%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[014/025] Loss: 0.79696552 Acc:79.96% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:78.16%\n",
      "best valacc:80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[015/025] Loss: 0.66844461 Acc:77.39% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:75.86%\n",
      "best valacc:80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[016/025] Loss: 0.69658349 Acc:79.20% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:77.78%\n",
      "best valacc:80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[017/025] Loss: 0.63059193 Acc:81.30% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.31%\n",
      "best valacc:80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[018/025] Loss: 0.62673587 Acc:80.53% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.69%\n",
      "best valacc:80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[019/025] Loss: 0.63485690 Acc:81.01% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:79.69%\n",
      "best valacc:80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:00<00:00, 75.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[020/025] Loss: 0.63296989 Acc:82.16% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:80.46%\n",
      "best valacc:80.08%\n",
      "Higher Valid Accuracy:%80.46%, Old Valid Accuracy:%80.08%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[021/025] Loss: 1.57734977 Acc:82.25% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:80.08%\n",
      "best valacc:80.46%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[022/025] Loss: 0.60808249 Acc:82.06% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:81.23%\n",
      "best valacc:80.46%\n",
      "Higher Valid Accuracy:%81.23%, Old Valid Accuracy:%80.46%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[023/025] Loss: 1.75234474 Acc:82.63% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:81.61%\n",
      "best valacc:81.23%\n",
      "Higher Valid Accuracy:%81.61%, Old Valid Accuracy:%81.23%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 5/5 [00:00<00:00, 95.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Epoch[024/025] Loss: 0.63575011 Acc:82.73% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:80.46%\n",
      "best valacc:81.61%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "Training: Epoch[025/025] Loss: 0.64818573 Acc:81.97% Lr:0.001\n",
      "迁移学习\n",
      "Valid set Accuracy:78.93%\n",
      "best valacc:81.61%\n",
      "模型PURE2DCNN的这轮训练0.00分钟\n",
      "===================Training Finished ======================\n",
      "==================================================读最佳模型==================================================\n",
      "./pathology/032370b/roi6/Split/proportion/Tr_0.0008/Va_0.0002/Te_0.999/1/image/PURE2DCNN/0.8040testprecition.pdf has been saved\n",
      "80.39966061076316\n",
      "86.82171407292698\n",
      "82.73999568823915\n",
      "84.07272812621022\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "modelname = 'PURE2DCNN' \n",
    "if modelname != 'TransGan':\n",
    "    resultpath = './pathology/032370b/roi2/Split/proportion/Tr_0.05/Va_0.01/Te_0.94/1/result/{}/'.format(modelname)\n",
    "else:\n",
    "    resultpath = './pathology/032370b/roi2/Split/proportion/Tr_0.05/Va_0.01/Te_0.94/1/result/{}/'.format(modelname)\n",
    "##############################\n",
    "##############################\n",
    "#######################\n",
    "if gpu_ids != -1:\n",
    "        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_ids)\n",
    "else:\n",
    "    while True:\n",
    "        try:\n",
    "            autoGPU(gpu_num, 5000)\n",
    "            break\n",
    "        except Exception as e :\n",
    "            print(e)\n",
    "            pass\n",
    "\n",
    "model = {\n",
    "        'SAE': SAE,\n",
    "        'PURE1DCNN': PURE1DCNN,\n",
    "        'PURE2DCNN':PURE2DCNN,\n",
    "        'PURE3DCNN': PURE3DCNN,\n",
    "        'DBDA': DBDA_network,\n",
    "        '1DCNN': _1DCNN,\n",
    "        'CDCNN':CDCNN,\n",
    "        'SSRN': SSRN_network,\n",
    "        'TransGan':NViT,\n",
    "        'NDIS_MODEL': NViT,\n",
    "        'NNDIS_MODEL':NNViT,\n",
    "        'DanfengViT': hongViT,\n",
    "        'SimpledanfengViT': hongViT(mode='ViT'),\n",
    "        'NViTBaseline': NViT(num_classes=2, depth=depth)}\n",
    "\n",
    "model = model[modelname]\n",
    "if isinstance(model, type):\n",
    "    model = model()\n",
    "    print('模型实例化')\n",
    "# spliteddata = splitdata(IMAGE, GND, datapaPUth , trainnum=eval(NTr), validnum=eval(NVa), testnum=eval(NTe))\n",
    "\n",
    "if modelname == 'TransGan':\n",
    "    model = torch.load(resultpath + 'bestmodel.pth')\n",
    "    print(resultpath + 'bestmodel.pth')\n",
    "else:\n",
    "    if load_bestmodel:\n",
    "        t_para = torch.load(resultpath + 'bestmodel.pth')\n",
    "        try:\n",
    "            model.load_state_dict(t_para)\n",
    "            print('读了最佳模型, 继续训练')\n",
    "        except:\n",
    "            model.load_state_dict({k.replace('module.', ''):v for k,v in t_para.items()})\n",
    "            print('读了最佳模型, 继续训练')\n",
    "    \n",
    "\n",
    "resultpath, imagepath, datapath, rootpath = setpath(dataset, trialnumber , NTr, NVa, NTe, modelname)\n",
    "# model.pos_embedding.requires_grad =  False   \n",
    "# model.cls_token.requires_grad = False\n",
    "# model.patch_to_embedding.requires_grad  = False\n",
    "# model.dropout.requires_grad = False\n",
    "# model.transformer.requires_grad = False\n",
    "# model.to_latent.requires_grad = False\n",
    "# model.mlp_head1.requires_grad =  False\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "# print(model.pos_embedding[0,0,:])\n",
    "    \n",
    "T = MyTrainProcess(model=model,\n",
    "                 modelname=modelname,\n",
    "                 processeddata=[patchdata, GND, spliteddata],\n",
    "                 train_config='./config_finetune.yaml',\n",
    "                 plotwrapper=[IMAGE, imagepath, '迁移学习'],\n",
    "                 resultpath=resultpath)\n",
    "T.training_start()\n",
    "# print(model.pos_embedding[0,0,:])\n",
    "T.evaluate(T.test_loader, T.test_result)\n",
    "imgPos = list(T.pos_dict['train']) + list(T.pos_dict['valid'])+ list(T.pos_dict['test'])\n",
    "imgGt = list(T.train_result.ylabel_pre + T.valid_result.ylabel_pre +  T.test_result.ylabel_pre)\n",
    "plot(imgPos, imgGt, IMAGE.shape, imagepath + '{:.4f}testprecition'.format(T.test_result.accuracy_score))\n",
    "print(T.test_result.accuracy_score*100)\n",
    "print(T.test_result.auc*100)\n",
    "print(T.test_result.precision*100)\n",
    "print(T.test_result.recall*100)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9c387163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<utils.DataResult at 0x7f22f5c8f610>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.train_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "523f752a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./pathology/032370b/roi5/Split/proportion/Tr_0.0008/Va_0.0002/Te_0.999/1/image/CDCNN/0.8039testprecition.pdf has been saved\n"
     ]
    }
   ],
   "source": [
    "imgPos = list(T.pos_dict['train']) + list(T.pos_dict['valid'])+ list(T.pos_dict['test'])\n",
    "imgGt = list(T.train_result.ylabel_pre + T.valid_result.ylabel_pre +  T.test_result.ylabel_pre)\n",
    "plot(imgPos, imgGt, IMAGE.shape, imagepath + '{:.4f}testprecition'.format(T.test_result.accuracy_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fc5a25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "metrics.balanced_accuracy_score(T.test_result.y_true, T.test_result.ylabel_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3021a70e",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgPos = np.array(processeddata['train'].pos + processeddata['valid'].pos + processeddata['test'].pos)\n",
    "imgGt = np.array(list(processeddata['train'].gt) + list(processeddata['valid'].gt) + T.test_result.ylabel_pre)\n",
    "\n",
    "img = np.zeros(IMAGE.shape[:2]+(1,), dtype=int)\n",
    "for p_idx, p in enumerate(imgPos):\n",
    "    img[tuple(p)] = imgGt[p_idx]\n",
    "\n",
    "\n",
    "patchradius = 30 \n",
    "\n",
    "result = deepcopy(T.test_result)\n",
    "for p_idx, p in enumerate(tqdm(processeddata['test'].pos)):\n",
    "\n",
    "    if p[0]>patchradius and p[0]<IMAGE.shape[0]-patchradius and \\\n",
    "        p[1]>patchradius and p[1]<IMAGE.shape[1]-patchradius:\n",
    "        sum = 0\n",
    "        for i in range(p[0]-patchradius, p[0]+patchradius+1):     \n",
    "            for j in range(p[1]-patchradius, p[1]+patchradius+1):                               \n",
    "                sum += img[i, j]\n",
    "        if sum >= (2*patchradius+1)**2*0.7:\n",
    "            result.ylabel_pre[p_idx] = 1\n",
    "            img[p[0], p[1]] = 1\n",
    "        elif sum <= (2*patchradius+1)**2*0.3:\n",
    "            result.ylabel_pre[p_idx] = 0\n",
    "            img[p[0], p[1]] = 0\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "699b05e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics.accuracy_score(result.ylabel_pre ,result.y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebf775c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from training_utils import *\n",
    "class MyTrainProcess(TrainProcess):\n",
    "    def training_start(self):\n",
    "        print('--------------------------训练----------------------------')\n",
    "        # 使用GPU\n",
    "        with open(self.train_config) as file:\n",
    "            dict = file.read()\n",
    "        config = yaml.load(dict, Loader=yaml.FullLoader)\n",
    "        learning_rate = config['learning_rate']\n",
    "        TRAIN_BATCHSIZE = config['train_batchsize'] \n",
    "        EPOCH = config['epoch']    \n",
    "        OPTIMIZER = config['optimization'] \n",
    "        if OPTIMIZER == 'Adam':\n",
    "            # optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)  \n",
    "            self.optimizer = optim.Adam(\n",
    "            self.model.parameters(),\n",
    "            lr=learning_rate,\n",
    "            betas=(0.9, 0.999),\n",
    "            eps=1e-8,\n",
    "            weight_decay=0)\n",
    "        elif OPTIMIZER == 'SGD':\n",
    "            self.optimizer = optim.SGD(self.model.parameters(), lr=learning_rate) \n",
    "        #  损失函数\n",
    "        # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
    "        #     optimizer, mode='min', factor=0.5, patience=10\n",
    "        # )\n",
    "        # # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        # #     optimizer, 15, eta_min=0.0, last_epoch=-1)\n",
    "        try:\n",
    "            with open(self.resultpath + 'result.pkl', 'rb') as f:\n",
    "                pklfile = pickle.load(f)\n",
    "            best_validacc = pklfile.accuracy_score \n",
    "            print('目前最佳精度{}'.format(best_validacc))\n",
    "\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            best_validacc = 0\n",
    "\n",
    "        plot_flag = False\n",
    "        for epoch in range(1, EPOCH+1):\n",
    "            start = time.time()\n",
    "            trainloss_sigma = 0.0\n",
    "            for batch_idx, data in enumerate(tqdm(self.train_loader)):\n",
    "                for idx, item in enumerate(data):\n",
    "                    data[idx] = item.to('cuda')    # 记录一个epoch的loss之和\n",
    "           \n",
    "                inputs, labels = data\n",
    "                # onehot_target = torch.eye(2)[labels.long(), :].to('cuda')\n",
    "                # labels = labels.unsqueeze(1)\n",
    "                self.optimizer.zero_grad()\n",
    "                # 清空梯度\n",
    "                self.model.train()\n",
    "                outputs = self.model(inputs)\n",
    "                # print(outputs)\n",
    "                # outputs.retain_grad()\n",
    "                if isinstance(outputs, torch.Tensor):\n",
    "                    if len(outputs.shape) == 1:\n",
    "                        loss = self.bceloss(outputs, labels.float()) \n",
    "                    else:\n",
    "                        loss = self.crossloss(outputs, labels.long())\n",
    "                else:\n",
    "                    if len(outputs[1].shape) == 1:\n",
    "                        loss = self.bceloss(outputs[1], labels.float()) \n",
    "                    else:\n",
    "                        loss = self.crossloss(outputs[1], labels.long())\n",
    "                # loss = self.criterion(outputs, onehot_target) \n",
    "                # print(self.evaluate(self.train_loader, self.train_result))\n",
    "                loss.backward()  # 反向传播 optimizer.step()  # 更新权值\n",
    "                self.optimizer.step()  \n",
    "                # 统计预测信息\n",
    "                trainloss_sigma += loss.item()\n",
    "                # 每 BATCH_SIZE 个 iteration 打印一次训练信息，loss为 BATCH_SIZE 个 iteration 的平均   \n",
    "            loss_avg = trainloss_sigma * TRAIN_BATCHSIZE / self.pos_dict['train'].shape[0] \n",
    "            train_acc = self.evaluate(self.train_loader, self.train_result)\n",
    "            print(\"Training: Epoch[{:03}/{:0>3}] Loss: {:.8f} Acc:{:.2%} Lr:{:.2}\".format(\n",
    "            epoch, EPOCH,  loss_avg, train_acc, self.optimizer.state_dict()['param_groups'][0]['lr']))\n",
    "            print(self.plotwrapper[2])\n",
    "            # scheduler.step(loss_avg)  # 更新学习率\n",
    "        # ------------------------------------ 观察模型在验证集上的表现 ------------------------------------\n",
    "            if self.valid_loader:\n",
    "                try:\n",
    "                    valid_acc= self.evaluate(self.valid_loader, self.valid_result)\n",
    "                    print('{} set Accuracy:{:.2%}'.format('Valid', valid_acc))\n",
    "                    print('best valacc:{:.2%}'.format(best_validacc))\n",
    "                    if valid_acc > best_validacc:\n",
    "                        plot_flag = True\n",
    "                        print(\"Higher Valid Accuracy:%{:.2%}, Old Valid Accuracy:%{:.2%}\".format(valid_acc, best_validacc))\n",
    "                        best_validacc = valid_acc\n",
    "                        self.bestmodel = deepcopy(self.model.state_dict())\n",
    "                        \n",
    "                    if self.pipesend and plot_flag and epoch%50==0:\n",
    "                        print('='*20 + 'saving model' + '='*20)\n",
    "                        torch.save(self.bestmodel, self.resultpath + 'bestmodel.pth') \n",
    "                        plot_flag = False\n",
    "                        self.pipesend.send(['plot', best_validacc])\n",
    "                \n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                end = time.time()       \n",
    "                print('模型{}的这轮训练{:.2f}分钟'.format(self.modelname, (end - start) / 60))\n",
    "        print('===================Training Finished ======================')\n",
    "       \n",
    "        self.model.load_state_dict(self.bestmodel)\n",
    "        print('='*50 + '读最佳模型' + '='*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538060b7",
   "metadata": {},
   "source": [
    "0.8794403076171875 0.9362004803556505 0.9644244900628951 0.8845101869418178"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e045a292",
   "metadata": {},
   "outputs": [],
   "source": [
    " processeddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b6ac9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "processeddata['test'].patch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3397206b",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = np.zeros(IMAGE.shape)\n",
    "for i, pos in enumerate(processeddata['test'].pos):\n",
    "    img[pos[0], pos[1]] = processeddata['test'].patch[i][4,4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c69bce91",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.gray()\n",
    "plt.imshow(IMAGE[:,:,20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af5db281",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgPos = list(processeddata['test'].pos)\n",
    "imgGt = list(processeddata['test'].gt)\n",
    "plot(imgPos, imgGt, IMAGE.shape, imagepath + 'groundtruth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce790b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultpath, imagepath, datapath = setpath(dataset, trialnumber , 0.0008, 0.0002, 0.999, modelname)\n",
    "imagepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98bf47e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "imagepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e36cdf26",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "resultpath, imagepath, datapath = setpath(dataset, trialnumber , 0.0008, 0.0002, 0.999, modelname)\n",
    "imgPos = list(processeddata['test'].pos)\n",
    "imgGt = list(T.test_result.ylabel_pre)\n",
    "plot(imgPos, imgGt, IMAGE.shape, imagepath + '{:.4f}testprecition'.format(T.test_result.accuracy_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92544bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "NViT = ViT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8d05944",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from einops import rearrange, repeat\n",
    "\n",
    "\n",
    "class Residual(nn.Module):\n",
    "    def __init__(self, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "    def forward(self, x, **kwargs):\n",
    "        return self.fn(x, **kwargs) + x\n",
    "\n",
    "class PreNorm(nn.Module):\n",
    "    def __init__(self, dim, fn):\n",
    "        super().__init__()\n",
    "        self.norm = nn.LayerNorm(dim)\n",
    "        self.fn = fn\n",
    "    def forward(self, x, **kwargs):\n",
    "        return self.fn(self.norm(x), **kwargs)\n",
    "\n",
    "class FeedForward(nn.Module):\n",
    "    def __init__(self, dim, hidden_dim, dropout = 0.):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(dim, hidden_dim),\n",
    "            nn.GELU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hidden_dim, dim),\n",
    "            nn.Dropout(dropout)\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        return self.net(x)\n",
    "\n",
    "class Attention(nn.Module):\n",
    "    def __init__(self, dim, heads, dim_head, dropout):\n",
    "        super().__init__()\n",
    "        inner_dim = dim_head * heads\n",
    "        self.heads = heads\n",
    "        self.scale = dim_head ** -0.5\n",
    "\n",
    "        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)\n",
    "        self.to_out = nn.Sequential(\n",
    "            nn.Linear(inner_dim, dim),\n",
    "            nn.Dropout(dropout)\n",
    "        )\n",
    "    def forward(self, x, mask = None):\n",
    "        # x:[b,n,dim]\n",
    "        b, n, _, h = *x.shape, self.heads\n",
    "\n",
    "        # get qkv tuple:([b,n,head_num*head_dim],[...],[...])\n",
    "        qkv = self.to_qkv(x).chunk(3, dim = -1)\n",
    "        # split q,k,v from [b,n,head_num*head_dim] -> [b,head_num,n,head_dim]\n",
    "        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)\n",
    "\n",
    "        # transpose(k) * q / sqrt(head_dim) -> [b,head_num,n,n]\n",
    "        dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale\n",
    "        mask_value = -torch.finfo(dots.dtype).max\n",
    "\n",
    "        # mask value: -inf\n",
    "        if mask is not None:\n",
    "            mask = F.pad(mask.flatten(1), (1, 0), value = True)\n",
    "            assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'\n",
    "            mask = mask[:, None, :] * mask[:, :, None]\n",
    "            dots.masked_fill_(~mask, mask_value)\n",
    "            del mask\n",
    "\n",
    "        # softmax normalization -> attention matrix\n",
    "        attn = dots.softmax(dim=-1)\n",
    "        # value * attention matrix -> output\n",
    "        out = torch.einsum('bhij,bhjd->bhid', attn, v)\n",
    "        # cat all output -> [b, n, head_num*head_dim]\n",
    "        out = rearrange(out, 'b h n d -> b n (h d)')\n",
    "        out = self.to_out(out)\n",
    "        return out\n",
    "\n",
    "class Transformer(nn.Module):\n",
    "    def __init__(self, \n",
    "                dim, \n",
    "                depth, \n",
    "                heads, \n",
    "                dim_head, \n",
    "                mlp_head, \n",
    "                dropout, ):\n",
    "        super().__init__()\n",
    "        self.depth = depth\n",
    "        self.layers = nn.ModuleList([])\n",
    "        self.pos_embedding = nn.Parameter(torch.randn(1, depth, 61*dim))\n",
    "        ##############\n",
    "        for _ in range(depth):\n",
    "            self.layers.append(nn.ModuleList([\n",
    "                Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),\n",
    "                Residual(PreNorm(dim, FeedForward(dim, mlp_head, dropout = dropout)))\n",
    "            ]))\n",
    "        ##############\n",
    "\n",
    "        #######捕获层之间关系########\n",
    "        self.catchlayer = nn.Sequential(\n",
    "                Residual(PreNorm(61*dim, Attention(61*dim, heads = 4, dim_head = 64, dropout = 0.1))),\n",
    "                Residual(PreNorm(61*dim, FeedForward(61*dim, 64, dropout = 0.1)))\n",
    "            )\n",
    "        \n",
    "        self.conv = nn.Conv2d(depth, 1, 1, stride=1)\n",
    "        # self.alpha = nn.Parameter(torch.randn(1, 61, dim)) \n",
    "        self.alpha = nn.Parameter(torch.randn(1)) \n",
    "\n",
    "\n",
    "\n",
    "    def forward(self, x, mask = None):\n",
    "        features = []  #原来的特征，64\n",
    "        for attn, ff in self.layers:\n",
    "            x = attn(x) \n",
    "            x = ff(x)\n",
    "            features.append(x)\n",
    "            \n",
    "        _, H, W = x.shape\n",
    "        for i in range(self.depth):\n",
    "            features[i] = features[i].flatten(1)\n",
    "\n",
    "        for i in range(self.depth):\n",
    "            features[i] = features[i].unsqueeze(1)\n",
    "            \n",
    "        FEA = torch.cat(features, dim=1)\n",
    "        FEA = FEA + self.pos_embedding\n",
    "        FEA = self.catchlayer(FEA)\n",
    "        FEA = rearrange(FEA, 'b n (h w) -> b n h w', h=H,w=W)\n",
    "        FEA = self.conv(FEA)\n",
    "        # x = x + torch.mul(self.alpha, FEA.squeeze())\n",
    "        x = x + FEA.squeeze()\n",
    "        # x = x + self.alpha*FEA.squeeze()\n",
    "        return x   # 按通道数合并特征   64 + 6*32=256\n",
    "\n",
    "class ViT(nn.Module):\n",
    "    def __init__(self, \n",
    "                image_size=9,\n",
    "                near_band=1, \n",
    "                num_patches=60, \n",
    "                num_classes=2, \n",
    "                dim=64, \n",
    "                depth=3, \n",
    "                heads=4, \n",
    "                mlp_dim=8, \n",
    "                pool='cls', \n",
    "                dim_head = 16, \n",
    "                dropout=0., \n",
    "                emb_dropout=0., \n",
    "                mode='CAF'):\n",
    "        super().__init__()\n",
    "\n",
    "        patch_dim = image_size ** 2 * near_band\n",
    "\n",
    "        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))\n",
    "        self.patch_to_embedding = nn.Linear(patch_dim, dim)\n",
    "        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))\n",
    "\n",
    "        self.dropout = nn.Dropout(emb_dropout)\n",
    "        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)\n",
    "\n",
    "        self.pool = pool\n",
    "        self.to_latent = nn.Identity()\n",
    "\n",
    "        self.mlp_head1 = nn.Sequential(\n",
    "            nn.LayerNorm(dim),\n",
    "            nn.Linear(dim, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "        self.mlp_head2 = nn.Sequential(\n",
    "            nn.LayerNorm(dim),\n",
    "            nn.Linear(dim, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "\n",
    "    def forward(self, x, mask = None):\n",
    "\n",
    "        # patchs[batch, patch_num, patch_size*patch_size*c]  [batch,200,145*145]\n",
    "        x = rearrange(x, 'b c h w -> b c (h w)')\n",
    "\n",
    "        ## embedding every patch vector to embedding size: [batch, patch_num, embedding_size]\n",
    "        x = self.patch_to_embedding(x) #[b,n,dim]\n",
    "        b, n, _ = x.shape\n",
    "\n",
    "        # add position embedding\n",
    "        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) #[b,1,dim]\n",
    "        x = torch.cat((cls_tokens, x), dim = 1) #[b,n+1,dim]\n",
    "        x += self.pos_embedding[:, :(n + 1)]\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        # transformer: x[b,n + 1,dim] -> x[b,n + 1,dim]\n",
    "        x = self.transformer(x, mask)\n",
    "\n",
    "        # classification: using cls_token output\n",
    "        x = self.to_latent(x[:,0])\n",
    "\n",
    "        # MLP classification layer\n",
    "        return self.mlp_head1(x).squeeze(), self.mlp_head2(x).squeeze()\n",
    "        # return self.mlp_head2(x).squeeze()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41bf0101",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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